---
title: "Climate Risk Lab IVU Dash"
author: ""
output:
flexdashboard::flex_dashboard:
orientation: rows
social: menu
source_code: embed
vertical_layout: fill
theme: cerulean
---
```{r setup, include=FALSE}
# Please ensure your data files are in the working directory before running the code
# Importing data
transition_risk_data = read.csv("Preparedness_of_countries_for_a_low-carbon_transition.csv")
physical_risk_data = read.csv("Climate-driven_INFORM_Risk.csv")
financial_risk_data = read.csv("Carbon_footprint_adjusted_loans_to_total_loans_for_deposit_takers.csv")
# Calling packages
suppressPackageStartupMessages(c(library(flexdashboard),
library(shinythemes),
library(tidyverse),
library(plotly),
library(maps),
library(leaflet),
library(leafpop),
library(purrr),
library(knitr),
library(DT)))
# Defining color pallete
#colors_palette = c("green4", "#6cc71c", "lawngreen", "greenyellow", "#adf20c", "#eff22e", "gold", "gold3", "#c75e1c", "#f52525", "brown")
#colors_palette = c("forestgreen", "limegreen", "green", "olivedrab1", "greenyellow", "yellow", "yellow3", "gold", "firebrick1", "red", "red4")
# Creating subsets
transition_risk_data = transition_risk_data %>%
dplyr::group_by(Country, Indicator)
transition_risk_data_exposure = transition_risk_data %>%
filter(Indicator == "Exposure")
transition_risk_data_resilience = transition_risk_data %>%
filter(Indicator == "Resilience")
# Light grey boundaries
l <- list(color = toRGB("grey"), width = 0.5)
g <- list(
showframe = F,
showcoastlines = TRUE,
coastlinecolor = "Black",
showland = TRUE,
landcolor = "White",
showocean= TRUE,
oceancolor ="deepskyblue",
showlakes= TRUE,
lakecolor ="deepskyblue",
showrivers= TRUE,
rivercolor ="deepskyblue",
projection = list(type = 'Mercator')
)
```
Row {.tabset .tabset-fade}
-----------------------------------------------------------------------
### Exposure to Transition Risk
```{r}
plot_exposure <- plot_geo(transition_risk_data_exposure)
plot_exposure %>% add_trace(
z = ~ transition_risk_data_exposure$F2019,
color = ~ transition_risk_data_exposure$F2019,
colors = 'Blues',
text = ~ transition_risk_data_exposure$Country,
locations = ~ transition_risk_data_exposure$ISO3,
marker = list(line = l)) %>%
colorbar(title = 'Exposure Index (as on 31-12-2019)', tickprefix = '') %>%
layout(
title = 'Exposure to Low-Carbon Economy Transition',
xaxis = list(title = 'Scale from 0 (high resilience) to 1 (low resilience)'),
geo = g
)
```
### Resilience to Transition Risk
```{r}
plot_resilience <- plot_geo(transition_risk_data_resilience)
plot_resilience %>% add_trace(
z = ~ transition_risk_data_resilience$F2019,
color = ~ transition_risk_data_resilience$F2019,
colors = 'Blues',
text = ~ transition_risk_data_resilience$Country,
locations = ~ transition_risk_data_resilience$ISO3,
marker = list(line = l)) %>%
colorbar(title = 'Resilience Index (as on 31-12-2019)', tickprefix = '') %>%
layout(
title = 'Resilience to Low-Carbon Economy Transition',
xaxis = list(title = 'Scale from 0 (high resilience) to 1 (low resilience)'),
geo = g
)
```